Master of Applied Artificial Intelligence & Data Science

This course is a one-semester course intended for graduate students in Data Science graduate programs. It is designed to introduce object-based programming and programming concepts with Python, Algorithm analysis and design, basic data structures, and Scientific computation and visualization with Python. The course will include the following sub topics; Introduction to Python and its basic data types; Object-oriented programming with Python; Recursion; Algorithm analysis; Searching and sorting; Abstract data types such as linked lists, stacks, queues, priority queues, trees, hashes and their implementation in Python; Vectors and matrices in Python; Plotting and visualization with Python; Solving systems of linear equations with Python. Students will gain experience through assignments and projects, with an emphasis on proper data science practices with appropriate applications. Outline of major topics: 1) Introduction to the Python interpreter, data types, and container data types, control structures using sequential, iterative and conditional execution; 2) functions, scopes and namespaces, recursion; 3) object-oriented programming with Python; 4) algorithm analysis, time and space complexity, basic notations, analyzing complexity of algorithms; 5) searching and sorting, linear search, binary search, bubble sort, selection sort, insertion sort, quicksort; 6) abstract data types, linked lists, stacks, queues, priority queues, trees, hashing and their implementation in Python; 7) towards data science and engineering with Python - vectors and matrices in Python; plotting and visualization with Python; solving systems of linear equations with Python; data storage; data analysis [All these items based on Numpy, Pandas, Scipy libraries].
Source note: the Program Specification Report contains an additional sentence in the DS 501 description that is visibly truncated in the original PDF; the intact description and the complete formal course learning outcomes are reproduced here.

Course learning outcomes
1. Evaluate, prepare, and analyze data using typical data science methods.
2. Design coding solutions using the Python language and relevant libraries.
3. Implement selected machine learning methods.
4. Recognize ethical issues and observe professional guidelines regarding the use of machine learning and applied artificial intelligence in data science.

The fundamental concepts of probability are essential to solve the data science problem. This course will introduce a broad range of probability theory and mathematical statistics that emphasizes the probabilistic foundations required to understand probability models and statistical. The topics covered include fundamentals of probability theory, statistical inference and graph data structure, including probabilistic models, random variables, useful distributions, expectations, the law of large numbers, the central limit theorem, point and confidence interval estimation, hypothesis tests, basic data structure and algorithms for graphs. By the end of the course the students will be expected to demonstrate the ability to apply fundamental concepts in exploratory data analysis, hypotheses tests, and be able to model problems in data science using graphs. Also, students will be able to use calculators and tables to perform simple statistical analyses for small samples and use popular python statistics packages to perform simple and sophisticated analyses for large samples.

Course learning outcomes
1. Apply fundamental concepts in exploratory data analysis, including random variable, probability of an event, additive rules, and conditional probability.
2. Understand several well-known distributions, including Binomial, Geometrical, Negative Binomial, Normal, and Exponential Distribution.
3. Derive the probability density function of transformations of random variables and use these techniques to generate data from various distributions.
4. Analyze hypotheses tests of means, proportions, and variances using both one- and two-sample data sets. Understanding of data science.

This course introduces fundamental concepts, design principles, and implementation techniques of database management systems. It covers data modeling, schema design, query languages, and integrity constraints, along with core system components such as storage, indexing, and query processing. The course emphasizes both theoretical foundations and practical considerations, including performance analysis and trade-offs in database system design. Students will explore how database systems represent, process, and optimize queries, and how different design choices impact efficiency and scalability. Hands-on components may include the use of existing database systems and/or implementation of selected database functionalities. The specific topics and tools may vary to reflect current practices and advances in the field.

Course learning outcomes
1. Use relational algebra and relational calculus, to express database queries.
2. Use query languages for database management systems.
3. Implement database tables, using functional dependencies and normal forms.
4. Explain core database system components, including storage, indexing, and query processing.
5. Analyze query execution strategies and query plans using appropriate algorithms and techniques.
6. Evaluate and analyze query performance using appropriate estimation methods and optimization techniques.
7. Compare alternative design and implementation choices in database systems and assess their trade-offs.
8. Compare different database systems and choose the suitable one for the given specifications.

This course aims to provide the students with a set of principles and skills that will be of paramount importance for conducting their research and disseminating the corresponding results. The course provides an overview of the scientific method and its application, project planning, working in teams, interaction with sources, and assessment of research results. Students are also taught the fundamental principles necessary for writing technical reports, scientific papers, and research proposals, and for preparing and delivering presentations: the focus of this part is on the connection between the employed research methods and the way in which the results are presented, rather than on the writing/presentation style in itself. Finally, students are exposed to a general introduction on moral reasoning, with subsequent focus on ethics in professional practice and research activity. The latter topic will focus on data management and intellectual property rights, and on the description of phenomena such as plagiarism and data fabrication.

Course learning outcomes
1. Asses a research plan and its rationale, including application of the scientific method, interaction with sources, and critical evaluation of research results.
2. Discuss the principles behind the production of reports, papers, proposals, and scientific presentations.
3. Arguing on ethical principles and their application to professional practice and research.

The AI for Business Intelligence (BI) course aims to equip students with the knowledge and skills to integrate artificial intelligence (AI) into BI processes. The curriculum covers key AI technologies including machine learning and natural language techniques and their applications in data analysis, predictive modeling, and decision support systems. Students will learn how to leverage AI to enhance data collection, processing, and visualization, enabling more informed and strategic business decisions. The course emphasizes practical applications, providing hands-on experience with BI tools and techniques relevant to contemporary business environments.

With the rapid advance of information technologies, data is emerging as the world's most important resource. Data-driven innovation offers a huge potential for economic, social and technological progress. In this course, students will learn and discuss concepts such as data-driven research and development, transforming traditional goods and services to data-intensive products, optimizing processes, logistics and delivery using data-driven techniques, and personalized marketing using data analytics, social networking and cloud computing. The course will also discuss how organizational evolution and optimization can be facilitated using data-driven techniques as an accelerator. The students will engage in a semester-long team-based project to create a data-driven innovation project proposal for a governmental or private organization.

Course learning outcomes
1. Explain the principles of data driven concepts and data management and engineering, and apply these principles when engineering data.
2. Analyze large amounts of data using a variety of data engineering tools.
3. Correctly interpret the results derived from these data-driven tools.

The course will start with setting the context by exploring the relationships between data mining, artificial intelligence, machine learning, deep learning, and statistics fields. It will then introduce knowledge representation, input preparation (preprocessing), fundamental data mining techniques (like probabilistic modeling, linear modeling, association mining, etc.), minimum description length (MDL) principle, validation techniques, decision trees, association rules, instance-based learning models, data transformations, data projections, Bayesian methods, and ensemble learning techniques (bagging, boosting, stacking, etc.) within the supervised, unsupervised and semi-supervised learning frameworks. Data visualization and warehousing methods along with examples from various application areas will also be visited to illustrate the use of data mining in decision support systems and processes.

Course learning outcomes
1. Understand a large set of supervised and unsupervised data mining algorithms along with their advantages and limitations.
2. Select an appropriate algorithm for a given problem and to quantitatively evaluate the fitness of a model generated by the selected algorithm.
3. Develop practical and trustable algorithmic solutions to be used by decision makers.
4. Be ready and well-prepared to participate, work in and/or scientifically contribute to related organizations and industries.

Students will work in teams on an AI project with an emphasis on teamwork, achievements, problem solving, reporting, continuous improvement, and regular deliverables. Initially, teams will review related work, and develop a project plan and design.The project will culminate with a full report describing the details, problems, challenges and results. Additionally a final presentation will be made by the group.

This course is a one-semester course intended for graduate students in Computer Science and Data Science graduate programs. It introduces the students to the concepts and existing models and techniques of deep learning. The main themes of the course are benefits, properties and challenges of deep learning; introduction to machine learning and optimization; challenges and common approaches; regularization; convolutional neural networks; deep recurrent networks and sequence learning; generative adversarial networks; autoencoders; advanced deep learning topics; applications. The students will learn how to design a deep learning architecture. The course presents several common deep learning techniques and exposes the students in learning how to use deep learning in a very efficient manner. Students will gain experience through assignments and projects, with an emphasis on proper deep learning practices with appropriate applications.

Course learning outcomes
1. Develop solutions using deep learning with Python programming language and environments to solve problems and perform specified tasks, using proper hyper parameters and optimization techniques.
2. Understand and apply concepts related to deep learning, such as logistics regression; gradient descent; regularization; Convolutional Neural Networks; Deep Recurrent Networks; Transformers; Generative Adversarial Networks; Autoencoders.
3. Critically determine how to select a deep learning approach for a domain and task.
4. Gain knowledge about state-of-the-art solutions using deep learning.
5. Understand the strengths and weaknesses of various deep learning approaches.

This course is a one-semester course intended for M.S. students in Computer Science and Data Science graduate programs. It enhances the student’s knowledge in computer vision concepts and approaches such as image formation, camera imaging geometry, feature detection and matching, stereo, image classification, scene understanding, and deep learning. In class, we will develop the intuitions and mathematics of the vision approaches. We will expose students to a number of real-world applications that are important to our daily lives. To further differentiate between theory and practice, we will guide students through a series of research projects such that they will have chance to implement cutting-edge computer vision algorithms. Students will also gain experience through research papers, with an emphasis on proper computer vision practices with appropriate applications.

Course learning outcomes
1. Understand and explain concepts related to computer vision such as image formation, feature detection and matching, stereo, image classification, scene understanding, and deep learning.
2. Recognize and describe both the theoretical and practical aspects of computing with images.
3. Successfully implement several interesting and cutting-edge vision algorithms which are widely used in real-world applications such as image retrieval, image captioning, face recognition, scene categorization, and activity recognition.
4. Understand the strengths and weaknesses of various computer vision approaches and apply them to new scenarios.
5. Read and understand scholarly articles in computer vision and come up with new ideas to solve the same problem, which may lead students to write their research paper in computer vision.

Students will learn a number of important topics in the field of computational biomedicine. They will study various topics within the biomedical domain, including primers of anatomy, physiology as well as the basis of various biomedical imaging techniques. Signal processing tools, such as filter theory, artifact rejection, as well as PCA and CSP will be covered. Additionally, we will cover how uni-as well as multi-variate features can be employed for decoding. Practical computing sessions will be carried out and students will also perform a project, where all research-related steps will be covered.

Course learning outcomes
1. Know some basic anatomical and physiological concepts.
2. Understand the basis of various biomedical imaging techniques, such as EEG, NIRS, fMRI, ECG, EMG, among others.
3. Understand which different imaging correlates can be measured and analyzed by various imaging modalities.
4. Understand and implement a range of data analytical techniques, that are common to biomedical related data analysis in particular, but also for computational approached in biomedicine in general.
5. Design and conduct a small biomedical study, i.e. experimental design, implementation, conduction, data analysis, report writing.

Computer networks are the core of modern communication. The activities of modern communications mostly run over Internet Protocol and are computer-controlled. Furthermore, with the usage, and rapid developments of wireless technologies such as cellular networks (including 3G, 4G, 5G, wireless LAN) and infrastructure-less technologies (Ad-Hoc Networks, WSNs), wireless communication systems have also become very important for modern communication. This course covers the distinct concepts and protocols in computer/communication network architectures. Addressing schemes, wireless variants of well-known routing and transport layer protocols, mobile IP and network design issues are considered.

Course learning outcomes
1. Review the distinct concepts and protocols of the wired/wireless network architectures and reflect on how they all fit together.
2. Assess the ways in which the technology of computer networks and wireless communication systems affect the suitability for particular applications.
3. Infer the implications of the specific modes of operation.
4. Test, simulate, and solve typical systems and their abstractions including analytical models for the quality of service analysis.
5. Review and assess security measures and countermeasures.
6. Adapt routing and IP addressing strategies in the design of computer networks to mobile systems.

The roots of computer science can be traced to modeling things such as trajectories of artillery shells and cryptographic protocols, both of which pushed the development of early computing systems in the early and mid-1940’s. A principal approach to modeling and simulation is abstraction, so that real world systems can be effectively simulated on a machine. In this course, we will examine modeling techniques such as Monte Carlo methods, stochastic processes, queuing theory, and Markov chains. Assessment and evaluation of approaches will also be covered. Important application areas that may be investigated include computer systems and networks, diagnostics, economics and finance, and urban planning.

Course learning outcomes
1. Acquire skills in handling situations involving more than one random variable and functions of random variables.
2. Apply basic probability techniques and models to analyze the performance of computer systems, and, in particular, of networks and queues.
3. Have a well – founded knowledge of standard distributions which can describe real life phenomena.
4. Understand and characterize phenomena which evolve with respect to time in a probabilistic manner.
5. Expose the basic characteristic features of a queuing system and acquire skills in analyzing queuing models.
6. Use discrete time Markov chains to model computer systems.
7. Analyze a network of queues with Poisson external arrivals, exponential service requirements and independent routing.

This course covers the latest advances in probabilistic, statistical and network-based computational models for real world problems such as computer vision and decision making. Topical coverage will begin at hidden Markov Models, through Markov networks, up to conditional random fields. Additionally, we will never cover novel advances in artificial neural networks such as convolutional neural networks ,deep learning models and recurrent neural networks.

Course learning outcomes
1. Utilize common probabilistic graphical models and analyze real-world problems, data.
2. Utilize efficiently the causal reasoning methods available to find problems in statistical reasoning.
3. Construct models from data, verify them and use them on test cases or other real-world situations.

Information retrieval (IR) is a discipline concerned with processing, storage, and search of information in large collections of unstructured documents. With development of the internet IR techniques have become a key enabler for accessing the vast amount of online information, with strong social and economic impact. This course is intended for students interested in theoretical foundations of IR and practical experience in building IR systems. During this course, we will discuss a wide variety of IR tasks, approaches, and tools. The course is accompanied by a set of practical assignments that reflect the main steps of the IR pipeline: document processing, indexing, search, and evaluation of results. In addition, we will discuss key research papers in the field of IR in a series of seminars.

Course learning outcomes
1. Demonstrate knowledge related to the main IR approaches and methods.
2. Design and conduct IR experiments, analyze and evaluate obtained results.
3. Implement main steps of IR pipeline: document processing, index construction, query processing, system evaluation.